Model, simulate, and analyze biological systems
SimBiology provides apps and programmatic tools to model, simulate, and analyze dynamic systems, focusing on pharmacokinetic/pharmacodynamic (PK/PD) and systems biology applications. It provides a block diagram editor for building models, or you can create models programmatically using the MATLAB® language. SimBiology includes a library of common PK models, which you can customize and integrate with mechanistic systems biology models.
A variety of model exploration techniques let you identify optimal dosing schedules and putative drug targets in cellular pathways. SimBiology uses ordinary differential equations (ODEs) and stochastic solvers to simulate the time course profile of drug exposure, drug efficacy, and enzyme and metabolite levels. You can investigate system dynamics and guide experimentation using parameter sweeps and sensitivity analysis. You can also use single subject or population data to estimate model parameters.
Specifying Model Dynamics
Use the drag-and-drop block diagram editor or programmatic tools to build QSP, PBPK, or PK/PD models. Import existing models from Systems Biology Markup Language (SBML) files.
Creating Model Variants
Use model variants to store a set of parameter values or initial conditions that differ from the base model configuration. Easily simulate virtual patients, drug candidates, alternate scenarios, and what-if hypotheses without creating multiple copies of your model.
Evaluating Dosing Strategies
Define and evaluate dosing strategies. Assess the benefits of combination therapies and determine optimal dosing strategies by combining dosing schedules that target different model species.
Automating Unit Conversion
Choose the units most appropriate for your model; for example, specify the dose amount in milligrams, drug concentration in nanograms/milliliter, and plasma volume in liters. Unit conversion tools convert all quantities in your model and data to a consistent unit system.
Accelerate simulation of large models or Monte Carlo simulations by converting models to compiled C code. Further improve performance by distributing simulations across multiple cores, clusters, or cloud computing resources using Parallel Computing Toolbox™.
Compute pharmacokinetic parameters of a drug from the time course measurements of drug concentrations without assuming a compartmental model. Perform NCA on both experimental and simulation data for single or multiple dosing, using sparse or serial sampling.
Estimate parameters using local or global estimation methods and calculate confidence intervals for parameters and model predictions. Fit each group independently to generate group-specific estimates or simultaneously fit all groups to estimate a single set of values.
Nonlinear-Mixed Effects Techniques (NLME)
Use NLME methods to fit population data using Stochastic Approximation of Expectation-Maximization (SAEM), first-order conditional estimate (FOCE), first-order estimate (FO), linear mixed-effects (LME) approximation, or restricted LME approximation.
Built-In Tasks and Interactive Exploration Tools
Use built-in analyses to analyze models. Use sliders to interactively explore the effects of variations in parameters or dose schedules on model outcomes.
Use SimBiology programmatically with MATLAB scripts to automate analyses and create custom analyses.
Creating Apps with SimBiology Desktop
Create standalone model exploration apps with one click using SimBiology Desktop.
Building Custom Apps
Create customized standalone apps using MATLAB app building capabilities.
Perform post-simulation calculations, for example to calculate area under the curve (AUC), and use it as a response for simulation, data fitting, or global sensitivity analysis
Global Sensitivity Analysis (GSA)
Explore the effects of variations in model quantities on model response by computing Sobol indices and by performing multiparametric GSA